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In 1942, Ralph Hartley proposed an analogue of the Fourier transform in his article "A More Symmetrical Fourier Analysis Applied to Transmission Problems".
Just like Fourier transform (FT), Hartley transform (HT) turns the original signal into a sum of trigonometric functions. But there is one significant difference between them. FT converts real values to complex numbers, while HT provides only real results. Because of this difference, the Hartley transform did not become popular - scientists and technicians did not see any advantages in it and continued to use the usual Fourier transform. In 1983, Ronald Bracewell presented a discrete version of the Hartley transform.

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The definition of a multi-currency Expert Advisor in this article is an Expert Advisor or trading robot that can trade (open orders, close orders and manage orders, for example: Trailing Stop Loss and Trailing Profit) for more than 1 symbol pair from only one symbol chart, where in this article Expert Advisor will trade for 30 pairs.

In this article we will use signals from two indicators, in this case Bollinger Bands® On Keltner Channel.

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In the previous article, we completed the implementation of a CSV file management class for storing and retrieving data related to financial markets. Having created the infrastructure, we are now ready to use this data to build and train a machine learning model.


Our task in this article is to implement a regression model that can predict the closing price of a financial asset within a week. This forecast will allow us to analyze market behavior and make informed decisions when trading financial assets.

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In the previous article, we started developing a multi-currency EA that works simultaneously with various trading strategies. At the first stage there were only two different strategies. They represented the implementation of the same trading idea, worked on the same trading instrument (symbol) and chart period (timeframe). They differed from each other only in the numerical values of the parameters.

We are now only interested in testing the suitability of this approach, and not the efficiency of its implementation. Therefore, within the framework of this article, we will try to develop at least some working implementation of this approach, which later will help us build a more beautiful one from an architectural point of view since we will already have knowledge on how to avoid mistakes.

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In the previous articles, we started developing a multi-currency EA that works simultaneously with various trading strategies. The solution provided in the second article is already significantly different from the one presented in the first one. This indicates that we are still in search of the best options.

Let's try to look at the developed system as a whole, abstracting from the small details of the implementation, in order to understand ways to improve it. To do this, let us trace the albeit short, but still noticeable evolution of the system.

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In this new article, I'm going to give you an ultimate introduction to an important tool that will add value to your programming skills. We'll look at Python Integration. When it comes to how this can be useful for us as developers it will depend on your objectives of software as Python is a high-level programming language that is easy to read, and it is simple as well. Python is a programming language that provides extensive libraries for areas such as data analysis, statistical computing, and machine learning. So, the integration of Python and MQL5 can provide better insights that can be helpful for financial market participants to improve their results through data processing and predictive analytics.

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In the previous articles, we have seen how powerful both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are and how they can be deployed to help beat the market by providing us with valuable trading signals.

In this one we are going to attempt combining two of the most powerful techniques CNN and RNN and observe their predictive impact in the stock market. But before that let us briefly understand what CNN and RNN are all about.

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Free good channels (which we can subscribe to) with some information about trading and coding, and for now - the channels about the articles and about new free tools in CodeBase
(it is recommended to subscribe because the tools are totally free by source codes):

RU - https://www.mql5.com/ru/channels/algo-trading-ru
EN - https://www.mql5.com/en/channels/algo-trading-en
ES - https://www.mql5.com/es/channels/algo-trading-es
FR - https://www.mql5.com/fr/channels/algo-trading-fr
PT - https://www.mql5.com/pt/channels/algo-trading-pt
DE - https://www.mql5.com/de/channels/algo-trading-de
IT - https://www.mql5.com/it/channels/algo-trading-it
TR - https://www.mql5.com/tr/channels/algo-trading-tr
JA - https://www.mql5.com/ja/channels/algo-trading-ja
ZH - https://www.mql5.com/zh/channels/algo-trading-zh
KO - https://www.mql5.com/ko/channels/algo-trading-ko

In the previous article "Neural Network in Practice: Pseudoinverse (I)", I showed how you can use a function available in the MQL5 library to calculate the pseudoinverse. However, the method present in the MQL5 library, as in many other programming languages, is intended to calculate the pseudoinverse when using matrices or at least some structure that may resemble a matrix.

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